How to Think Computationally About AI, the Universe and Everything | Stephen Wolfram | TED

Summary

Stephen Wolfram explores the foundational role of computation in understanding the universe, proposing a 'computational universe' model. He discusses the emergence of space-time, gravity, and quantum mechanics from simple computational rules, introducing the concept of 'rulial space' where all possible computations exist. Wolfram highlights the computational language's potential to empower human and AI exploration of this vast space, emphasizing the importance of defining human goals within it.

Takeaways

• 🌟 Computation is a fundamental way to formalize the world, alongside human language, mathematics, and logic.
• 🏗️ The speaker has dedicated 50 years to building a tower of science and technology based on the idea of computation.
• 🤔 A central question posed is whether computation is the underlying fabric of the universe, which was affirmatively answered in 2020.
• 📚 The concept of space being made of discrete elements is foundational to the idea that everything is defined by a network of relations between these space 'atoms'.
• 🌌 The universe's emergence can be visualized through simple computational rules, suggesting a computational basis for reality.
• 🔬 Quantum mechanics and gravity are shown to emerge from computational processes, challenging traditional scientific paradigms.
• 🕰️ The idea of 'branchial space' introduces a multi-threaded view of time and history, offering a new perspective on quantum mechanics.
• 🔮 Four paradigms of world modeling are identified, evolving from material composition to computational irreducibility and multi-computational systems.
• 🧠 The human mind's perception of the universe is limited by computational bounds and a sense of persistence in time.
• 🌐 The 'rulial' concept presents a vast, abstract space where all possible computational processes occur, with observers sampling specific slices.
• 🛠️ The Wolfram Language is described as a full-scale computational language that encapsulates the intellectual achievements of civilization, providing a tool for expressing and operationalizing ideas.
• 🤖 AI's potential to explore 'rulial space' is highlighted, but the importance of aligning AI exploration with human understanding and goals is emphasized.
• 🔮 Generative AI can offer insights into the vastness of rulial space by creating and exploring concepts beyond human comprehension.
• 🔄 The societal implications of computational irreducibility are significant, suggesting a future where we cannot predict the outcomes of complex systems.
• 🌟 The advancement of AI and computation does not diminish the importance of human desires and goals, but rather enhances our ability to achieve them.
• 📈 As automation increases, it opens up new directions for exploration and activity in the rulial space, requiring human input to define meaningful paths.
• 🌱 The future of work is likely to be less about technical execution and more about conceptualization and broad thinking, facilitated by computational language.

Q & A

What is the central idea proposed in the script about the nature of the universe?

The central idea is that the universe is fundamentally computational, with everything starting from the idea that space and matter are made of discrete elements, and the structure of space and everything in it is defined by a network of relations between these elements.

What is the significance of the 'ultimate machine code of the universe' mentioned in the script?

The 'ultimate machine code of the universe' signifies a discovery that the universe operates on a computational basis, suggesting a fundamental framework that underlies all physical phenomena.

How does the script relate the concept of computation to the emergence of space-time and gravity?

The script suggests that the application of simple computational rules can lead to the emergence of space-time and Einstein's equations for gravity, indicating that these phenomena are inherently computational in nature.

What is the role of observers in the script's explanation of quantum mechanics?

Observers play a crucial role as they are embedded in the universe and experience the branching and merging of quantum mechanics. The script describes quantum mechanics as the story of how branching minds perceive a branching universe.

What are the four broad paradigms for making models of the world mentioned in the script?

The four paradigms are: 1) Modeling based on what things are made of, without considering time. 2) Using mathematical formulas where time is a coordinate value. 3) Starting with simple computational rules and letting them run, leading to computational irreducibility. 4) A multi-computational approach with many threads of time that can only be understood by an observer.

What is the 'rulial space' and how does it relate to the concept of the 'rulid'?

The 'rulial space' is a conceptual space where all possible computational processes exist. The 'rulid' is the entangled limit of all these processes, representing everything computationally possible.

How does the script describe the relationship between observers and the laws of physics?

The script posits that the laws of physics, such as general relativity, quantum mechanics, and statistical mechanics, are perceived by observers with certain characteristics, such as being computationally bounded and persistent in time.

What is the role of generative AI in exploring the 'rulial space' as described in the script?

Generative AI can be used to take a tiny slice of the 'rulial space' and generate content based on human-produced images, helping to provide intuition and understanding of the vast computational possibilities within the 'rulial space'.

How does the script discuss the potential of AIs in the context of 'rulial space'?

The script suggests that AIs can explore 'rulial space' and perform 'ruliology', but without human alignment, they may perform tasks that are irrelevant or uninteresting to humans.

What is the significance of the 'Wolfram Language' in the script's narrative?

The 'Wolfram Language' is presented as a full-scale computational language that encapsulates intellectual achievements and allows for the operationalization of ideas in computational terms, providing a tool for both humans and AIs to harness computational superpowers.

How does the script address the societal implications of AI and computational irreducibility?

The script raises the dilemma of balancing the potential of AI with the need for predictability and control. It suggests that as AI becomes more autonomous, its actions may become increasingly difficult to predict, leading to societal challenges in determining how to best utilize AI.

What is the script's perspective on the future of human work in an increasingly automated world?

The script suggests that as automation advances, it opens up new areas for human endeavor. It emphasizes the importance of defining what humans want and using computational language to harness the power of AI and automation to achieve those goals.

Outlines

00:00

🧠 The Computational Universe and Our Place in It

Stephen Wolfram explores the idea that computation is the fundamental process underlying the universe. He discusses his journey over 50 years in science and technology, culminating in the discovery of what he believes to be the 'ultimate machine code of the universe' in April 2020. This discovery supports the theory that space and everything within it is composed of discrete elements with simple computational rules dictating their interactions. Wolfram explains how the universe can be seen as emerging from these basic computational processes, leading to the emergence of space-time and gravity, as well as quantum mechanics, which he describes as the story of how branching minds perceive a branching universe.

05:01

🌌 The Ruliad: A Multidimensional Computational Landscape

This paragraph delves into the concept of the 'rulial space,' a computational landscape where all possible computational processes occur. Wolfram explains that observers, including humans, are part of this space and perceive only specific slices of it due to our computational limitations and our sense of persistence in time. He introduces the idea that the laws of physics we observe, including general relativity, quantum mechanics, and statistical mechanics, are a direct result of our characteristics as observers within the ruliad. Wolfram also discusses the potential for AI to explore the ruliad, but notes that without alignment with human understanding, much of what AIs discover may be irrelevant to us.

10:01

🛠 The Power of Computational Language in Human and AI Advancement

Stephen Wolfram discusses the development of a computational language, which he sees as a key to harnessing the power of computation. He emphasizes the importance of this language in enabling both humans and AIs to express concepts and ideas computationally, leading to advancements across various fields. Wolfram highlights the success of the Wolfram Language, which encapsulates intellectual achievements in a computational form, providing a means to formalize knowledge about the world. He also touches on the societal implications of AI and computation, noting the challenges of predictability and control in a world where systems may exhibit computational irreducibility.

15:03

🚀 Embracing the Future of Automation and Computational Exploration

In the final paragraph, Wolfram reflects on the future of automation and the role of AI in exploring the vastness of the ruliad. He suggests that as automation progresses, it will open up new directions for human endeavor rather than rendering us obsolete. Wolfram emphasizes the importance of defining human goals and using computational language to chart our path through the ruliad. He concludes by highlighting the accessibility of the power and depth of the ruliad to everyone, provided they learn to harness the computational superpowers available to them.

Keywords

💡Computation

Computation refers to the process of performing mathematical calculations, including those performed by computers. In the context of the video, computation is presented as a fundamental way to formalize and understand the universe. The script suggests that computation is not just a tool but the underlying fabric of the universe, with everything from space to quantum mechanics emerging from computational rules.

💡WolframAlpha

WolframAlpha is a computational knowledge engine that answers factual queries directly by computing the answer from curated data. In the script, it is mentioned as a significant project launched by the speaker, which aims to provide answers to questions by applying computational methods. It represents a practical application of the computational paradigm discussed in the video.

💡Space-Time

Space-time is a concept in physics that combines the three dimensions of space and the one dimension of time into a single four-dimensional continuum. The script discusses how space-time and Einstein's equations for gravity emerge from the application of simple computational rules, suggesting a computational basis for the fabric of the universe.

💡Quantum Mechanics

Quantum mechanics is a fundamental theory in physics that describes the behavior of matter and energy at the smallest scales. In the video, quantum mechanics is depicted as emerging from the computational rules that govern the universe, specifically as the story of how branching minds perceive a branching universe, illustrating the deep connection between computation and the nature of reality.

💡Branchial Space

Branchial space is a term used in the script to describe the space of quantum branches, which is a conceptual framework for understanding quantum mechanics within the computational universe. It represents the idea that the universe's history can branch and merge, and our perception of it is influenced by our position as observers within this branching structure.

💡Computational Irreducible

Computational irreducibility is the concept that some processes cannot be predicted or simplified; they must be run in their entirety to understand their outcome. The script uses this term to discuss the limits of predictability in computational systems, including the universe itself, and how this irreducibility affects our understanding and interaction with complex systems.

💡Ruliad

The ruliad, as introduced in the script, is a term for the entangled limit of all possible computational processes. It represents the totality of all computationally possible outcomes and is a key concept in understanding the scope and potential of computation in the universe. The script suggests that everything we perceive is a result of our position within the ruliad.

💡General Relativity

General relativity is Einstein's theory of gravity, which describes the curvature of space-time due to mass and energy. In the video, it is mentioned as one of the key theories of 20th-century physics that emerges from the computational rules governing the universe, highlighting the video's theme of computation as a foundational concept.

💡Statistical Mechanics

Statistical mechanics is a branch of physics that uses statistical methods to explain the thermodynamic properties of systems in terms of their constituent particles. The script includes it as one of the theories that emerge from the computational perspective, specifically mentioning its second law, which relates to the increase of entropy in a system.

💡Generative AI

Generative AI refers to artificial intelligence systems that can create new content, such as images, text, or music. In the script, generative AI is mentioned as a tool for exploring the ruliad by taking a tiny slice of it and generating content based on human-provided concepts, demonstrating the potential of AI to interact with and expand our understanding of the computational universe.

💡Wolfram Language

The Wolfram Language is a computational language developed by Wolfram Research, which is designed to provide a high-level way to express computational ideas. The script discusses the Wolfram Language as a full-scale computational language that encapsulates various facets of human knowledge in computational terms, allowing users to harness computational power to create and understand complex systems.

Highlights

Computation is a powerful formalization method alongside human language, mathematics, and logic.

The speaker has dedicated 50 years to building a tower of science and technology based on computation.

WolframAlpha was launched 13 years ago, and a decade later, the ultimate machine code of the universe was announced as computational.

The universe is conceptualized as being made of discrete space-time elements with a network of relations.

The emergence of space and everything in it is demonstrated through simple computational rules.

Black holes and gravitational waves are shown to emerge from pure computation.

Quantum mechanics is presented as the story of how branching minds perceive a branching universe.

Four broad paradigms for making models of the world are identified, including the new paradigm of multi-computational systems.

The 'ruliad' is introduced as the entangled limit of all possible computational processes.

Observers are part of the ruliad and perceive laws that align with 20th-century physics theories.

Different minds are visualized as being at different places in 'rulial space'.

Generative AI is used to explore the ruliad with tiny slices aligned with human images.

The richness of simple rules in ruliology is highlighted, but the challenge is connecting it with human understanding.

AIs can explore rulial space, but their achievements are most significant when aligned with human interests.

The development of the Wolfram Language is described as a full-scale computational language that encapsulates intellectual achievements.

Computational language provides a new path for creating 'computational X' for all fields, beyond traditional computer science.

The societal dilemma of AI's full computational potential and the unpredictability it brings is discussed.

The historical trend of automation opening up new directions in the ruliad is highlighted.

The importance of defining human goals in the computational age and the role of computational language in this process is emphasized.

The concept of 'promptocracy' is introduced as a potential method for societal decision-making in the AI era.

The Wolfram Language is positioned as a tool for conceptualization and harnessing computational superpowers.

Transcripts

Human language, mathematics, logic.

These are all ways to formalize the world.

And in our century,

there's a new and yet more powerful one: computation.

For nearly 50 years,

I've had the great privilege

of building up an ever-taller tower of science and technology

that's based on that idea of computation.

And so today, I want to tell you a little bit about what that's led to.

There's a lot to talk about, so I'm going to go quickly.

And sometimes I'm going to summarize in a sentence

what I've written a whole book about.

But you know,

I last gave a TED talk 13 years ago,

in February 2010,

soon after WolframAlpha launched,

and I ended that talk with a question.

Question was,

is computation ultimately what's underneath everything

in our universe?

I gave myself a decade to find out.

And actually, it could have needed a century.

But in April 2020, just after the decade mark,

we were thrilled to be able to announce

what seems to be the ultimate machine code of the universe.

And yes, it's computational.

So computation isn't just a possible formalization,

it's the ultimate one for our universe.

It all starts from the idea that space, like matter, is made of discrete elements,

and from that structure of space and everything in it,

it's defined just by a network of relations

between these elements that we might call atoms of space.

So it's all very elegant, but deeply abstract.

But here's kind of a humanized representation,

a version of the very beginning of the universe.

And what we're seeing here is the emergence of space

and everything in it

by the successive application of very simple computational rules.

And remember, these dots are not atoms in any existing space.

They're atoms of space that get put together to make space.

And yes, if we kept going long enough,

we could build our whole universe this way.

So eons later,

here's a chunk of space with two little black holes

that, if we wait a little while, will eventually merge,

generating little ripples of gravitational radiation.

And remember, all of this is built from pure computation.

But like fluid mechanics emerging from molecules,

what emerges here is space-time and Einstein's equations for gravity,

though there are deviations that we just might be able to detect,

like that the dimensionality of space won't always be precisely three.

And there's something else.

Our computational rules can inevitably be applied in many ways,

each defining a different kind of thread of time,

a different path of history that can branch and merge.

But as observers embedded in this universe,

we're branching and merging, too.

And it turns out that quantum mechanics emerges as the story

of how branching minds perceive a branching universe.

So the little pink lines you might be able to see here

show the structure of what we call branchial space,

the space of quantum branches.

And one of the stunningly beautiful things,

at least for physicists like me,

is that the same phenomenon that in physical space gives us gravity,

in branchial space gives us quantum mechanics.

So in the history of science so far,

I think we can identify sort of four broad paradigms

for making models of the world that can be distinguished

kind of by how they deal with time.

So in antiquity and in plenty of areas of science, even today,

it's all about kind of, what are things made of.

And time doesn't really enter.

But in the 1600s came the idea of modeling things

with mathematical formulas in which time enters,

but basically just as a coordinate value.

Then in the 1980s, and this is something in which I was deeply involved,

came the idea of making models

by starting with simple computational rules

and just letting them run.

So can one predict what will happen?

No.

There's what I call computational irreducibility,

in which, in effect, the passage of time corresponds to an irreducible computation

that we have to run in order to work out how it will turn out.

But now there's kind of something,

something even more -- in our physics project,

there’s things that have become multi-computational,

with many threads of time

that can only be knitted together by an observer.

So it's kind of a new paradigm that actually seems to unlock things

not only in fundamental physics,

but also in the foundations of mathematics and computer science,

and possibly in areas like biology and economics as well.

So I talked about building up the universe

by repeatedly applying a computational rule.

But how is that rule picked?

Well, actually it isn't,

because all possible rules are used,

and we're building up what I call the ruliad,

the kind of deeply abstract but unique object

that is the entangled limit of all possible computational processes.

Here's a tiny fragment of it shown in terms of Turing machines.

So this ruliad is everything.

And we as observers are necessarily part of it.

In the ruliad as a whole,

in a sense, everything computationally possible can happen.

But observers like us just sample specific slices of the ruliad.

And there are two crucial facts about us.

First, we're computationally bounded, our minds are limited,

and second, we believe we're persistent in time,

even though we're made of different atoms of space at every moment.

So then, here's the big result.

What observers with those characteristics perceive in the ruliad

necessarily follows certain laws.

And those laws turn out to be precisely

the three key theories of 20th century physics:

general relativity, quantum mechanics,

and statistical mechanics in the second law.

So it's because we're observers like us

that we perceive the laws of physics we do.

We can think of sort of different minds

as being at different places in rulial space.

Human minds who think alike are nearby,

animals further away,

and further out, we get to kind of alien minds

where it's hard to make a translation.

So how can we get intuition for all of this?

Well, one thing we can do is use generative AI

to take what amounts to an incredibly tiny slice of the ruliad

aligned with images we humans have produced.

We can think of this as sort of a place in the ruliad

described by using the concept of a cat in a party hat.

So zooming out, we saw there

what we might call Cat Island.

Pretty soon we’re in a kind of an inter-concept space.

Occasionally things will look familiar,

but mostly, what we'll see is things we humans don't have words for.

In physical space, we explore the universe

by sending out spacecraft.

In rulial space, we explore more

by expanding our concepts and our paradigms.

We can kind of get a sense of what's out there

by sampling possible rules,

doing what I call ruliology.

So even with incredibly simple rules,

there's incredible richness.

But the issue is that most of it doesn't yet connect

with things we humans understand or care about.

It's like when we look at the natural world

and only gradually realize that we can use features of it for technology.

So even after everything our civilization has achieved,

we're just at the very, very beginning of exploring rulial space.

What about AIs?

Well, just like we can do ruliology,

AIs can in principle go out and explore rulial space.

Left to their own devices, though,

they'll mostly just be doing things

we humans don't connect with or care about.

So the big achievements of AI in recent times

have been about making systems that are closely aligned with us humans.

We train LLMs on billions of web pages so they can produce texts

that's typical of what we humans write.

And yes, the fact that this works

is undoubtedly telling us some deep scientific things

about the semantic grammar of language

and generalizations of things like logic

that perhaps we should have known centuries ago.

You know, for much of human history,

we were kind of like the LLMs,

figuring things out by kind of matching patterns in our minds.

But then came more systematic formalization and eventually computation.

And with that, we got a whole other level of power to truly create new things

and to, in effect, go wherever we want in the ruliad.

But the challenge is to do that in a way that connects with what we humans,

and our AIs, understand.

In fact, I've devoted a large part of my life

to kind of trying to build that bridge.

It's all been about creating a language for expressing ourselves computationally,

a language for computational thinking.

The goal is to formalize what we know about the world in computational terms,

to have computational ways to represent cities and chemicals and movies

and humor and formulas and our knowledge about them.

It’s been a vast undertaking that spanned more than four decades of my life,

but it's something very unique and different.

But I'm happy to report that in what has been Mathematica

and is now the Wolfram Language,

I think we firmly succeeded in creating

a truly full-scale computational language.

In effect,

every one of these functions here can be thought of as formalizing

and encapsulating, in computational terms,

some facet of the intellectual achievements of our civilization.

It's sort of the most concentrated form of intellectual expression that I know,

sort of finding the essence of everything and coherently expressing it

in the design of our computational language.

For me personally,

it's been an amazing journey, kind of, year after year,

building the sort of tower of ideas and technology that's needed.

And nowadays sharing that process with the world

in things like open live streams and so on.

A few centuries ago,

the development of mathematical notation,

and what amounts to the language of mathematics,

gave a systematic way to express math and made possible algebra and calculus,

and eventually all of modern mathematical science.

And computational language now provides a similar path,

letting us ultimately create a computational X

for all imaginable fields X.

I mean, we've seen the growth of computer science, CS,

but computational language opens up something ultimately much bigger

and broader, CX.

I mean, for 70 years we've had programming languages

which are about telling computers in their terms what to do.

But computational language

is about something intellectually much bigger.

It's about taking everything we can think about

and operationalizing it in computational terms.

You know, I built the Wolfram Language

first and foremost because I wanted to use it myself.

And now when I use it,

I feel like it's kind of giving me some kind of superpower.

I just have to imagine something in computational terms.

And then the language sort of almost magically lets me bring it into reality,

see its consequences, and build on them.

And yes, that's the sort of superpower

that's let me do things like our physics project.

And over the past 35 years,

it's been my great privilege to share this superpower with many other people,

and by doing so,

to have enabled an incredible number of advances across many fields.

It's sort of a wonderful thing to see people, researchers, CEOs, kids,

using our language to fluently think in computational terms,

kind of crispening up their own thinking,

and then in effect, automatically calling in computational superpowers.

And now it's not just people who can do that.

AIs can use our computational language as a tool, too.

Yes, to get their facts straight,

but even more importantly, to compute new facts.

There are already some integrations of our technology into LLMs.

There's a lot more you'll be seeing soon.

And, you know, when it comes to building new things

in a very powerful emerging workflow,

it's basically to start by telling the LLM roughly what you want,

then to have it try to express that in precise Wolfram Language,

then, and this is a critical feature of our computational language,

compared to, for example, programming language,

you as a human can read the code,

and if it does what you want,

you can use it as kind of a dependable component to build on.

OK, but let's say we use more and more AI,

more and more computation.

What's the world going to be like?

From the industrial revolution on,

we’ve been used to doing engineering where we can in effect,

see how the gears mesh to understand how things work.

But computational irreducibility

now shows us that that won't always be possible.

We won't always be able to make a kind of simple human or, say,

mathematical narrative

to explain or predict what a system will do.

And yes, this is science, in effect, eating itself from the inside.

From all the successes of mathematical science,

we've come to believe that somehow, if we only could find them,

there'd be formulas to kind of predict everything.

But now computational irreducibility shows us that that isn't true.

And that in effect, to find out what a system will do,

we have to go through the same irreducible computational steps

as the system itself.

Yes, it's a weakness of science,

but it's also why the passage of time is significant and meaningful

and why we can't just sort of jump ahead to get the answer.

We have to live the steps.

It's actually going to be, I think, a great societal dilemma of the future.

If we let our AIs achieve their kind of full computational potential,

they'll have lots of computational irreducibility

and we won't be able to predict what they'll do.

But if we put constraints on them to make them more predictable,

we'll limit what they can do for us.

So what will it feel like if our world is full of computational irreducibility?

Well, it's really nothing new

because that's the story with much of nature.

And what's happened there

is that we've found ways to operate within nature,

even though nature can sometimes still surprise us.

And so it will be with the AIs.

We might give them a constitution, but there will always be consequences

we can't predict.

Of course, even figuring out societally what we want from the AIs is hard.

Maybe we need you know, a promptocracy

where people write prompts instead of just voting.

But basically, every control the outcome scheme

seems full of both political philosophy

and computational irreducibility gotchas.

You know, if we look at the whole arc of human history,

the one thing that's systematically changed

is that more and more gets automated.

And LLMs just gave us a dramatic and unexpected example of that.

So what does that mean?

Does that mean that in the end, us humans will have nothing to do?

Well, if we look at history,

what seems to happen is that when one thing gets automated away,

it opens up lots of new things to do.

And as economies develop,

the pie chart of occupations seems to get more and more fragmented.

And now we're back to the ruliad.

Because at a foundational level,

what's happening is that automation is opening up more directions

to go in the ruliad.

But there's no abstract way to choose between these.

It's a question of what we humans want,

and it requires kind of humans doing work to define that.

So a society of AI as sort of untethered by human input,

would effectively go off and explore the whole ruliad.

But most of what they do would seem to us random and pointless,

much like most of nature doesn't seem to us right now,

like it's achieving a purpose.

I mean, one used to imagine that to build things that are useful to us,

we'd have to do it kind of step by step.

But AI and the whole phenomenon of computation

tell us that really what we need

is more just to define what we want.

Then computation, AI, automation can make it happen.

And yes, I think the key to defining in a clear way what we want

is computational language.

And, you know, even after 35 years,

for many people,

Wolfram Language is still sort of an artifact from the future.

If your job is to program, it seems like a cheat.

How come you can do in an hour what would usually take you a week?

But it can also be kind of daunting because having dashed off that one thing,

you now have to conceptualize the next thing.

Of course, it's great for CEOs and CTOs

and intellectual leaders who are ready to race on to the next thing.

And indeed, it's an impressively popular thing in that set.

In a sense, what's happening is that Wolfram Language shifts

from concentrating on mechanics to concentrating on conceptualization,

and the key to that conceptualization is broad computational thinking.

So how can one learn to do that?

It's not really a story of CS,

it's really a story of CX.

And as a kind of education,

it's more like liberal arts than STEM.

It's part of a trend that when you automate technical execution,

what becomes important is not figuring out how to do things,

but what to do.

And that's more a story of broad knowledge and general thinking

than any kind of narrow specialization.

You know, there's sort of an unexpected human centeredness to all of this.

We might have thought that with the advance of science and technology,

the particulars of us humans would become ever less relevant.

But we've discovered that that's not true, and that, in fact, everything,

even our physics,

depends on how we humans happen to have sampled the ruliad.

Before our physics project,

we didn't know if our universe really was computational,

but now it's pretty clear that it is.

And from that, we're sort of inexorably led to the ruliad,

with all its kind of vastness

so hugely greater than the physical space in our universe.

So where will we go in the ruliad?

Computational language is what lets us chart our path.

It lets us humans define our goals and our journeys.

And what's amazing is that all the power and depth

of what's out there in the ruliad is accessible to everyone.

One just has to learn to harness those computational superpowers,

which kind of starts here,

you know, our portal to the ruliad.

Thank you.

(Applause)